eshansurendra/cell_anomaly_detection_using_autoencoders
This repository offers a TensorFlow-based anomaly detection system for cell images using adversarial autoencoders, capable of identifying anomalies even in contaminated datasets. Check out our code, pretrained models, and papers for more details.
This project helps medical technicians or researchers automatically identify abnormal cells in microscopy images. You provide a collection of cell images, and the system learns what 'normal' cells look like. It then flags any new images that deviate significantly from this learned normal pattern as potentially anomalous, even if your initial training data wasn't perfectly clean.
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Use this if you need to reliably detect unusual cell images in a large dataset, especially when the definition of 'normal' might be subtly complex or your training data might contain a few outliers.
Not ideal if you need to classify cells into many different known categories or if you're working with data types other than images.
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Jun 17, 2024
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